Minimum-violation LTL Planning with Conflicting Specifications
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Abstract (arXiv)
We consider the problem of automatic generation of control strategies for robotic vehicles given a set of high-level mission specifications, such as "Vehicle x must eventually visit a target region and then return to a base," "Regions A and B must be periodically surveyed," or "None of the vehicles can enter an unsafe region." We focus on instances when all of the given specifications cannot be reached simultaneously due to their incompatibility and/or environmental constraints. We aim to find the least-violating control strategy while considering different priorities of satisfying different parts of the mission. Formally, we consider the missions given in the form of linear temporal logic formulas, each of which is assigned a reward that is earned when the formula is satisfied. Leveraging ideas from the automata-based model checking, we propose an algorithm for finding an optimal control strategy that maximizes the sum of rewards earned if this control strategy is applied. We demonstrate the proposed algorithm on an illustrative case study.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2013.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
7 papers.
- 2025Safety Guardrails for LLM-Enabled Robotscited by 30
- 2021Safety of the Intended Driving Behavior Using Rulebookscited by 8
- 2022Receding Horizon Planning with Rule Hierarchies for Autonomous Vehiclescited by 7
- 2024RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fieldscited by 5
- 2023Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectivescited by 2
- 2024RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shiftscited by 2
- 2026Commonsense-Grounded Path Planning from Abstract Instructionscited by 0
- 2026Commonsense-Grounded Path Planning from Abstract Instructions19 Sep 2026
- 2025Safety Guardrails for LLM-Enabled Robots10 Mar 2025
- 2024RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields3 Dec 2024
- 2024RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts18 May 2024
- 2023Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives23 Apr 2023
- 2022Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles6 Dec 2022
- 2021Safety of the Intended Driving Behavior Using Rulebooks10 May 2021